Issue 42, 2024

A colorimetric-aptamer-based assay for the determination of enrofloxacin through triggering the aggregation of gold nanoparticles

Abstract

Although enrofloxacin (ENR) is a widely used broad-spectrum antibiotic in veterinary medicine, its residues in animals can pose a risk to human health. Thus, we developed a new method for detecting ENR based on aptamers and AuNPs. In the absence of ENR, the aptamers attached to the surface of the AuNPs via electrostatic interactions to protect the AuNPs from NaCl, and the solution remained red. Conversely, the aptamer bonded with ENR, leading the aptamer to detach from the AuNP surface, and the color of the solution changed from red to blue. Based on this principle, ENR can be qualitatively detected by the naked eye and quantitatively detected by measuring the absorbance ratio at 650 nm and 530 nm. The experimental results showed a good linear relationship within the ENR concentration range of 0–400 nM, with a limit of detection (LOD) of 1.72 nM, which is satisfactory for detection in food safety. Additionally, this method has also been successfully applied to the detection of ENR in tap water, river water, milk, serum and urine, with good recovery rates and RSD values of less than 7%, indicating its great potential for ENR detection in environmental water samples. More importantly, the combination of this method with a smartphone platform provided great convenience for on-site and visual detection of ENR, offering promising applicability prospects.

Graphical abstract: A colorimetric-aptamer-based assay for the determination of enrofloxacin through triggering the aggregation of gold nanoparticles

Supplementary files

Article information

Article type
Paper
Submitted
04 Qad 2024
Accepted
10 Way 2024
First published
23 Way 2024

Anal. Methods, 2024,16, 7121-7129

A colorimetric-aptamer-based assay for the determination of enrofloxacin through triggering the aggregation of gold nanoparticles

W. Kong, C. Li, A. Zhang, X. Li, Q. Gong, B. Jin, X. Jia, X. Liu and Y. Kang, Anal. Methods, 2024, 16, 7121 DOI: 10.1039/D4AY01259C

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